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madhava-yallanki
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detr_finetuned_cppe5

Object Detection
Transformers
Safetensors
detr
Generated from Trainer
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  2. model.safetensors +1 -1
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: facebook/detr-resnet-50
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: detr_finetuned_cppe5
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # detr_finetuned_cppe5
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+
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+ This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.6375
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+ - Map: 0.0618
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+ - Map 50: 0.1727
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+ - Map 75: 0.0707
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+ - Map Small: 0.0
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+ - Map Medium: 0.1642
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+ - Map Large: 0.0627
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+ - Mar 1: 0.0908
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+ - Mar 10: 0.1208
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+ - Mar 100: 0.1608
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+ - Mar Small: 0.0
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+ - Mar Medium: 0.1983
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+ - Mar Large: 0.2208
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+ - Map Coverall: 0.2826
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+ - Mar 100 Coverall: 0.625
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+ - Map Face Shield: 0.0
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+ - Mar 100 Face Shield: 0.0
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+ - Map Gloves: 0.023
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+ - Mar 100 Gloves: 0.1625
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+ - Map Goggles: 0.0
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+ - Mar 100 Goggles: 0.0
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+ - Map Mask: 0.0034
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+ - Mar 100 Mask: 0.0167
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: cosine
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+ - num_epochs: 30
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:|
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+ | No log | 1.0 | 2 | 3.6370 | 0.0139 | 0.0385 | 0.0064 | 0.0 | 0.0447 | 0.0205 | 0.015 | 0.065 | 0.0725 | 0.0 | 0.13 | 0.075 | 0.0694 | 0.35 | 0.0 | 0.0 | 0.0002 | 0.0125 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | No log | 2.0 | 4 | 3.3372 | 0.0084 | 0.023 | 0.0106 | 0.0 | 0.0261 | 0.0077 | 0.0 | 0.08 | 0.0975 | 0.0 | 0.185 | 0.0833 | 0.0404 | 0.4 | 0.0 | 0.0 | 0.0016 | 0.0875 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | No log | 3.0 | 6 | 3.2809 | 0.0175 | 0.0686 | 0.0096 | 0.0 | 0.0692 | 0.0062 | 0.01 | 0.08 | 0.0975 | 0.0 | 0.21 | 0.075 | 0.0862 | 0.4 | 0.0 | 0.0 | 0.0015 | 0.0875 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | No log | 4.0 | 8 | 3.0734 | 0.0305 | 0.0958 | 0.0172 | 0.0 | 0.1039 | 0.0224 | 0.05 | 0.0925 | 0.1025 | 0.0 | 0.21 | 0.0833 | 0.149 | 0.425 | 0.0 | 0.0 | 0.0035 | 0.0875 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | No log | 5.0 | 10 | 3.1212 | 0.0098 | 0.0457 | 0.007 | 0.0 | 0.0514 | 0.0082 | 0.01 | 0.05 | 0.1 | 0.0 | 0.18 | 0.0917 | 0.0476 | 0.425 | 0.0 | 0.0 | 0.0015 | 0.075 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | No log | 6.0 | 12 | 2.9352 | 0.0084 | 0.03 | 0.0086 | 0.0 | 0.0386 | 0.0073 | 0.0 | 0.08 | 0.115 | 0.0 | 0.18 | 0.1167 | 0.0405 | 0.5 | 0.0 | 0.0 | 0.0014 | 0.075 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | No log | 7.0 | 14 | 2.8460 | 0.0099 | 0.039 | 0.002 | 0.0 | 0.0451 | 0.0084 | 0.0 | 0.0875 | 0.1275 | 0.0 | 0.18 | 0.125 | 0.044 | 0.5 | 0.0 | 0.0 | 0.0054 | 0.1375 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | No log | 8.0 | 16 | 2.8413 | 0.0071 | 0.0285 | 0.0028 | 0.0 | 0.0164 | 0.0149 | 0.005 | 0.095 | 0.13 | 0.0 | 0.15 | 0.1583 | 0.0273 | 0.5 | 0.0 | 0.0 | 0.0082 | 0.15 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | No log | 9.0 | 18 | 2.6854 | 0.0123 | 0.0493 | 0.0054 | 0.0 | 0.0219 | 0.0249 | 0.01 | 0.095 | 0.135 | 0.0 | 0.15 | 0.1833 | 0.0517 | 0.5 | 0.0 | 0.0 | 0.01 | 0.175 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | No log | 10.0 | 20 | 2.7651 | 0.0344 | 0.0962 | 0.0379 | 0.0 | 0.0791 | 0.0287 | 0.03 | 0.1025 | 0.155 | 0.0 | 0.2 | 0.1833 | 0.0629 | 0.5 | 0.0 | 0.0 | 0.0082 | 0.175 | 0.0 | 0.0 | 0.101 | 0.1 |
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+ | No log | 11.0 | 22 | 2.7936 | 0.0226 | 0.0829 | 0.018 | 0.0 | 0.0438 | 0.0431 | 0.035 | 0.09 | 0.1 | 0.0 | 0.155 | 0.1333 | 0.1078 | 0.375 | 0.0 | 0.0 | 0.005 | 0.125 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | No log | 12.0 | 24 | 2.7867 | 0.0216 | 0.0842 | 0.0055 | 0.0 | 0.0647 | 0.0309 | 0.015 | 0.09 | 0.12 | 0.0 | 0.16 | 0.1542 | 0.1029 | 0.475 | 0.0 | 0.0 | 0.005 | 0.125 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | No log | 13.0 | 26 | 2.9817 | 0.0112 | 0.0372 | 0.0055 | 0.0 | 0.0442 | 0.0144 | 0.0 | 0.0975 | 0.1175 | 0.0 | 0.16 | 0.1417 | 0.0509 | 0.475 | 0.0 | 0.0 | 0.0053 | 0.1125 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | No log | 14.0 | 28 | 2.9225 | 0.0278 | 0.1074 | 0.0148 | 0.0 | 0.0775 | 0.0425 | 0.005 | 0.1067 | 0.1617 | 0.0 | 0.1817 | 0.2 | 0.1119 | 0.65 | 0.0 | 0.0 | 0.01 | 0.125 | 0.0 | 0.0 | 0.0168 | 0.0333 |
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+ | No log | 15.0 | 30 | 2.8893 | 0.0347 | 0.1374 | 0.0193 | 0.0 | 0.0831 | 0.0566 | 0.0558 | 0.1092 | 0.1442 | 0.0 | 0.1817 | 0.1792 | 0.1356 | 0.55 | 0.0 | 0.0 | 0.0126 | 0.1375 | 0.0 | 0.0 | 0.0252 | 0.0333 |
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+ | No log | 16.0 | 32 | 2.8845 | 0.0328 | 0.1313 | 0.0151 | 0.0 | 0.088 | 0.0427 | 0.0617 | 0.1092 | 0.1442 | 0.0 | 0.1817 | 0.1792 | 0.1163 | 0.55 | 0.0 | 0.0 | 0.0139 | 0.1375 | 0.0 | 0.0 | 0.0337 | 0.0333 |
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+ | No log | 17.0 | 34 | 2.8390 | 0.0383 | 0.1469 | 0.0128 | 0.0 | 0.1033 | 0.0365 | 0.02 | 0.1125 | 0.1475 | 0.0 | 0.19 | 0.1792 | 0.1056 | 0.55 | 0.0 | 0.0 | 0.0356 | 0.1375 | 0.0 | 0.0 | 0.0505 | 0.05 |
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+ | No log | 18.0 | 36 | 2.8089 | 0.0364 | 0.1392 | 0.0129 | 0.0 | 0.0877 | 0.0362 | 0.0175 | 0.1125 | 0.1525 | 0.0 | 0.19 | 0.1875 | 0.1296 | 0.575 | 0.0 | 0.0 | 0.027 | 0.1375 | 0.0 | 0.0 | 0.0252 | 0.05 |
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+ | No log | 19.0 | 38 | 2.8321 | 0.0329 | 0.1149 | 0.0352 | 0.0 | 0.0884 | 0.0327 | 0.0117 | 0.1142 | 0.1542 | 0.0 | 0.2017 | 0.2 | 0.1363 | 0.6 | 0.0 | 0.0 | 0.0199 | 0.1375 | 0.0 | 0.0 | 0.0084 | 0.0333 |
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+ | No log | 20.0 | 40 | 2.8311 | 0.0498 | 0.1556 | 0.0568 | 0.0 | 0.1641 | 0.0433 | 0.0417 | 0.115 | 0.155 | 0.0 | 0.205 | 0.2 | 0.2192 | 0.6 | 0.0 | 0.0 | 0.0181 | 0.125 | 0.0 | 0.0 | 0.0118 | 0.05 |
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+ | No log | 21.0 | 42 | 2.7355 | 0.0488 | 0.1556 | 0.0538 | 0.0 | 0.1617 | 0.0444 | 0.0417 | 0.1117 | 0.1517 | 0.0 | 0.1967 | 0.2 | 0.2192 | 0.6 | 0.0 | 0.0 | 0.0166 | 0.125 | 0.0 | 0.0 | 0.0084 | 0.0333 |
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+ | No log | 22.0 | 44 | 2.6950 | 0.0484 | 0.1527 | 0.0538 | 0.0 | 0.1605 | 0.0451 | 0.0383 | 0.1167 | 0.1567 | 0.0 | 0.2017 | 0.2125 | 0.2192 | 0.6 | 0.0 | 0.0 | 0.0181 | 0.15 | 0.0 | 0.0 | 0.0049 | 0.0333 |
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+ | No log | 23.0 | 46 | 2.6900 | 0.0484 | 0.1527 | 0.0538 | 0.0 | 0.1605 | 0.045 | 0.0383 | 0.1167 | 0.1567 | 0.0 | 0.2017 | 0.2125 | 0.2191 | 0.6 | 0.0 | 0.0 | 0.0182 | 0.15 | 0.0 | 0.0 | 0.0049 | 0.0333 |
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+ | No log | 24.0 | 48 | 2.6738 | 0.0481 | 0.1539 | 0.0538 | 0.0 | 0.1598 | 0.0451 | 0.0383 | 0.1133 | 0.1533 | 0.0 | 0.1933 | 0.2125 | 0.2192 | 0.6 | 0.0 | 0.0 | 0.0182 | 0.15 | 0.0 | 0.0 | 0.0034 | 0.0167 |
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+ | No log | 25.0 | 50 | 2.6657 | 0.061 | 0.1663 | 0.0707 | 0.0 | 0.1603 | 0.0632 | 0.0883 | 0.1208 | 0.1608 | 0.0 | 0.1983 | 0.2208 | 0.2826 | 0.625 | 0.0 | 0.0 | 0.0192 | 0.1625 | 0.0 | 0.0 | 0.0034 | 0.0167 |
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+ | No log | 26.0 | 52 | 2.6576 | 0.0627 | 0.1831 | 0.0707 | 0.0 | 0.1632 | 0.0627 | 0.0875 | 0.1208 | 0.1608 | 0.0 | 0.1983 | 0.2208 | 0.2826 | 0.625 | 0.0 | 0.0 | 0.0288 | 0.1625 | 0.0 | 0.0 | 0.0021 | 0.0167 |
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+ | No log | 27.0 | 54 | 2.6461 | 0.0631 | 0.1856 | 0.0707 | 0.0 | 0.1642 | 0.0628 | 0.0908 | 0.1208 | 0.1608 | 0.0 | 0.1983 | 0.2208 | 0.2827 | 0.625 | 0.0 | 0.0 | 0.0295 | 0.1625 | 0.0 | 0.0 | 0.0034 | 0.0167 |
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+ | No log | 28.0 | 56 | 2.6412 | 0.0618 | 0.1727 | 0.0707 | 0.0 | 0.1642 | 0.0627 | 0.0908 | 0.1208 | 0.1608 | 0.0 | 0.1983 | 0.2208 | 0.2826 | 0.625 | 0.0 | 0.0 | 0.023 | 0.1625 | 0.0 | 0.0 | 0.0034 | 0.0167 |
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+ | No log | 29.0 | 58 | 2.6383 | 0.0618 | 0.1727 | 0.0707 | 0.0 | 0.1642 | 0.0627 | 0.0908 | 0.1208 | 0.1608 | 0.0 | 0.1983 | 0.2208 | 0.2826 | 0.625 | 0.0 | 0.0 | 0.023 | 0.1625 | 0.0 | 0.0 | 0.0034 | 0.0167 |
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+ | No log | 30.0 | 60 | 2.6375 | 0.0618 | 0.1727 | 0.0707 | 0.0 | 0.1642 | 0.0627 | 0.0908 | 0.1208 | 0.1608 | 0.0 | 0.1983 | 0.2208 | 0.2826 | 0.625 | 0.0 | 0.0 | 0.023 | 0.1625 | 0.0 | 0.0 | 0.0034 | 0.0167 |
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+
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+ ### Framework versions
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+
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+ - Transformers 4.49.0
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+ - Pytorch 2.6.0
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+ - Datasets 3.3.2
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+ - Tokenizers 0.21.0
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